Real-Time Ventricular Fibrillation Detection Using an Embedded Microcontroller in a Pervasive Environment

Abstract

Many healthcare problems are life threatening and need real-time detection to improve patient safety. Heart attack or ventricular fibrillation (VF) is a common problem worldwide. Most previous research on VF detection has used ECG devices to capture data and sent to other higher performance units for processing and has relied on domain experts and/or sophisticated algorithms for detection. In this case, it delayed the response time and consumed much more energy of the ECG module. In this study, we propose a prototype that an embedded microcontroller where an ECG sensor is used to capture, filter and process data, run VF detection algorithms, and only transmit the detected event to the smartphone for alert and call for services. We discuss how to adapt a common filtering and scale process and five light-weighted algorithms from open literature to realize the idea. We also develop an integrated prototype, which emulates the VF process from existing data sets, to evaluate the detection capability of the framework and algorithms. Our results show that (1) TD outperforms the other four algorithms considered with sensitivity reaching 96.56% and specificity reaching 81.53% in the MIT-BIH dataset. Our evaluations confirm that with some adaptation the conventional filtering process and detection algorithms can be efficiently deployed in a microcontroller with good detection accuracy while saving battery power, shortening response time, and conserving the network bandwidth.

Real-Time Embedded Systems: Present and Future

“…Efficient digital signal processing is an important enabler for advanced embedded applications in many domains. Of the five articles in this special issue that address efficient embedded DSP and applications, the work in Reference [7] studies feasibility options and evaluates the performance of fully embedded algorithms for real-time ventricular fibrillation detectors which can send timely alerts without requiring external processing, for applications in pervasive health monitoring. Health monitoring applications are also the focus of Reference [8] that proposes an efficient embedded hardware accelerator for long-term bio-signal monitoring and compression, which makes it suitable for various Internet of Things (IoT) applications.…”

Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy

“…The CNN-based arrhythmia classification could eliminate the cumbersome requirement of criteria selections and parameters setting in traditional MLbased arrhythmia detection methods while achieving high detection accuracy. Some notable prior arts (Silva et al, 2019) implementing CNN architecture for arrhythmia classification, reports the use of various architectural layers (Kwon et al, 2018), attention on noise removal, use of LSTM networks (Krasteva et al, 2020), etc. The most recent work reporting the highest accuracy to date uses a bidirectional LSTM (bi-LSTM) instead of unidirectional LSTM (Jeon et al, 2020).…”

A Task Parameter Inference Framework for Real-Time Embedded Systems

“…Real-Time Embedded Systems (RTES) have been deployed in time-critical environments, often involving control tasks each of which invokes a series of jobs with periodic releases, which has been widely studied in the industrial informatics community [1][2][3][4][5][6]. While traditional RTES have been little exposed to security attacks due to isolation from the external world and use of specialized hardware/protocol, increased connectivity yields new security attacks on RTES.…”